Biometric Identification Using the Periocular Region

Abstract

The face is the fundamental basis for the identification/recognition of a person. Recognition of a individual by the face data and methods to extract the unique facial feature points from the facial image have been significantly increased during the last decade. The periocular region has become the powerful alternative for unconstrained biometrics with better robustness and high discrimination ability. In this paper, multiple descriptors are used for deriving the discriminative features of the periocular region and city block distance is used to compute the similarity between the feature vectors. Feature extraction techniques employed in recognition using the periocular region are Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Speeded Up Robust Feature (SURF). Results of Periocular modality are then compared with the results of face patterns. Periocular region showed significant accuracies compared to face with only using 25% of the full face. Experimentations are carried on FRGC database, and accuracies of both the periocular and face regions are compared.